Our experiments use three publicly available data sets.
First, we use two MovieLens data sets ML100K and ML1M [1]}.
We choose ML100K and ML1M because it includes demographic attributes of users such as gender, age, occupation, zipcode and also the timestamp needed for our temporal splitting.
We used zipcode to generate the State attribute.
In order to convert MovieLens data from explicit feedback to implicit feedback, we set a cutoff\( >= 3\) , such that items with ratings \(>=3\)  are defined to be relevant, and the rest as non-relevant.
Then, we pre-processed the resulting implicit data such that we have at least 20 interactions per user.
ML100K subset contains 845 users and a total of 1574 movies for 80961 interactions.
ML1M subset contains a total of 5755 users and 3624 movies for 831745 interactions.
We also use a subset of LastFM [2]}, a music data set.
We use artists as the items.
We preprocessed LastFM data, retaining only users who listened to at least 20 artists and artists to which at least 10 users have listened.
The result is a subset of 836 users and 12k artists.
For each user in LastFM data, gender and country location attributes are provided.
We used the Country attribute to generate the Continent and the EU vs Rest attributes.
We choose these data sets because they contain user attributes and they are publicly available.
Table REF  summarizes the statistics of the data sets.
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